3D Parametric Human Recovery.
Also known as human pose and shape estimation, 3D parametric human recovery leverages human parametric model representation (such as SMPL [1]}, SMPL-X [2]}, STAR [3]} and GHUM [4]}) that achieves sophisticated mesh reconstruction with a small amount of parameters. Existing methods take keypoints [5]}, [2]}, [7]}, images [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, videos [15]}, [16]}, [17]}, [18]}, [19]}, [20]} as the input to obtain the parameters. Recently, point clouds have become more popular [21]}, [22]}, [23]}, [24]}, [25]} for both parametric human and clothing recovery.
Apart from those that provide keypoints, various datasets also provide ground-truth SMPL parameters. MoSh [26]} is applied on Human3.6M [27]} to generate SMPL annotations. CMU Panoptic [28]} and HUMBI [29]} leverages keypoints from multiple camera views. 3DPW [30]} combines a mobile phone and inertial measurement units (IMUs). Synthetic dataset such as AGORA [31]} renders high-quality human scans in virtual environments and fits SMPL to the original mesh. Video games have also become an alternative source of data [32]}, [33]}. In addition to SMPL parameters that do not model clothes or texture, HuMMan also provides textured meshes of clothed subjects.
